2018 IEEE Real-Time Systems Symposium (RTSS)最新文献

The SRP Resource Sharing Protocol for Self-Suspending Tasks 自挂起任务资源共享协议SRP

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00051

Geoffrey Nelissen, Alessandro Biondi

引用次数: 6

Work-in-Progress: Enhanced Energy-Aware Standby-Sparing Techniques for Fixed-Priority Hard Real-Time Systems 正在进行的工作:用于固定优先级硬实时系统的增强能源感知备用节省技术

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00031

Linwei Niu, Jonathan Musselwhite, Wei Li

引用次数: 3

Optimizing Network Calculus for Switched Ethernet Network with Deficit Round Robin 亏缺轮询交换以太网的网络演算优化

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00046

Aakash Soni, Xiaoting Li, Jean-Luc Scharbarg, C. Fraboul

{"title":"Optimizing Network Calculus for Switched Ethernet Network with Deficit Round Robin","authors":"Aakash Soni, Xiaoting Li, Jean-Luc Scharbarg, C. Fraboul","doi":"10.1109/RTSS.2018.00046","DOIUrl":"https://doi.org/10.1109/RTSS.2018.00046","url":null,"abstract":"Avionics Full Duplex switched Ethernet (AFDX) is the de facto standard for the transmission of critical avionics flows. It is a specific switched Ethernet solution based on First-in First-out (FIFO) scheduling. Worst-case traversal time (WCTT) analysis is mandatory for such flows, since timing constraints have to be guaranteed. A classical approach in this context is Network Calculus (NC). However, NC introduces some pessimism in the WCTT computation. Moreover, the worst-case often corresponds to very rare scenarios. Thus, the network architecture is most of the time lightly loaded. Typically, less than 10 % of the available bandwidth is used for the transmission of avionics lows on an AFDX network embedded in an aircraft. One solution to improve the utilization of the network is to introduce Quality of Service (QoS) mechanisms. Deficit Round Robin (DRR) is such a mechanism and it is envisioned for future avionics networks. A WCTT analysis has been proposed for DRR. It is based on NC. It doesn't make any assumption on the scheduling of flows by end systems. The first contribution of this paper is to identify sources of pessimism of this approach and to propose an improved solution which removes part of this pessimism. The second contribution is to show how the scheduling of flows can be integrated in this optimized DRR approach, thanks to offsets. An evaluation on a realistic case study shows that both contributions bring significantly tighter bounds on worst-case latencies.","PeriodicalId":294784,"journal":{"name":"2018 IEEE Real-Time Systems Symposium (RTSS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125443159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Work-in-Progress: Real-Time Modeling for Intrusion Detection in Automotive Controller Area Network 在研:汽车控制器局域网入侵检测的实时建模

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00030

Habeeb Olufowobi, Gedare Bloom, C. Young, Joseph Zambreno

引用次数: 9

Deadline-Based Scheduling for GPU with Preemption Support 基于截止日期的GPU调度与抢占支持

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00021

Nicola Capodieci, R. Cavicchioli, M. Bertogna, Aingara Paramakuru

{"title":"Deadline-Based Scheduling for GPU with Preemption Support","authors":"Nicola Capodieci, R. Cavicchioli, M. Bertogna, Aingara Paramakuru","doi":"10.1109/RTSS.2018.00021","DOIUrl":"https://doi.org/10.1109/RTSS.2018.00021","url":null,"abstract":"Modern automotive-grade embedded computing platforms feature high-performance Graphics Processing Units (GPUs) to support the massively parallel processing power needed for next-generation autonomous driving applications (e.g., Deep Neural Network (DNN) inference, sensor fusion, path planning, etc). As these workload-intensive activities are pushed to higher criticality levels, there is a stronger need for more predictable scheduling algorithms that are able to guarantee predictability without overly sacrificing GPU utilization. Unfortunately, the real-rime literature on GPU scheduling mostly considered limited (or null) preemption capabilities, while previous efforts in broader domains were often based on programming models and APIs that were not designed to support the real-rime requirements of recurring workloads. In this paper, we present the design of a prototype real-time scheduler for GPU activities on an embedded System on a Chip (SoC) featuring a cutting edge GPU architecture by NVIDIA adopted in the autonomous driving domain. The scheduler runs as a software partition on top of the NVIDIA hypervisor, and it leverages latest generation architectural features, such as pixel-level preemption and threadlevel preemption. Such a design allowed us to implement and test a preemptive Earliest Deadline First (EDF) scheduler for GPU tasks providing bandwidth isolations by means of a Constant Bandwidth Server (CBS). Our work involved investigating alternative programming models for compute APIs, allowing us to characterize CPU-to-GPU command submission with more detailed scheduling information. A detailed experimental characterization is presented to show the significant schedulability improvement of recurring real-time GPU tasks.","PeriodicalId":294784,"journal":{"name":"2018 IEEE Real-Time Systems Symposium (RTSS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122205240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

Work-in-Progress: Incorporating Deadline-Based Scheduling in Tasking Programming Model for Extreme-Scale Parallel Computing 在制品:在极端规模并行计算的任务规划模型中纳入基于期限的调度

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00022

A. Cheng, Panruo Wu

{"title":"Work-in-Progress: Incorporating Deadline-Based Scheduling in Tasking Programming Model for Extreme-Scale Parallel Computing","authors":"A. Cheng, Panruo Wu","doi":"10.1109/RTSS.2018.00022","DOIUrl":"https://doi.org/10.1109/RTSS.2018.00022","url":null,"abstract":"Processing and analyzing big data sets updated in real time in an increasing number of applications such as severe weather prediction and particle-physics experiments require the computational power of extreme-scale high-performance computing (HPC) systems. To address the scheduling of massive task/thread sets on these extreme-scale systems, current strategies rely on improving centralized, distributed, and parallel scheduling algorithms as well as virtualization developed for HPC systems which aim to reduce the makespan and balance the load among the computing nodes in these systems. However, these HPC schedulers provide no guarantees on meeting timing constraints such as deadlines that are required in an increasing number of these real-time science workflows. This paper describes a new project which departs from this established trend of best-effort scheduling of large-scale HPC Message Passing Interface (MPI) tasks and ensemble workloads found in fine-grain many-task computing (MTC) applications. The new approach brings real-time scheduling to address the demands of real-time science workloads. This new framework abstracts information about the tasks or threads, and continuously dispatch this workload to meet deadlines and other timing constraints associated with individual tasks or groups of tasks in extreme-scale HPC systems to reduce execution time and energy consumption. This paper introduces deadline-based scheduling in the tasking programming model.","PeriodicalId":294784,"journal":{"name":"2018 IEEE Real-Time Systems Symposium (RTSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127910442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks 基于背包的AVR任务最坏情况需求计算方法

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00053

Sandeep Kumar Bijinemula, Aaron Willcock, Thidapat Chantem, N. Fisher

引用次数: 2

Work-in-Progress: Preference-Oriented Scheduling in Multiprocessor Real-Time Systems 在制品:多处理器实时系统中面向偏好的调度

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00023

Qin Xia, Dakai Zhu, Hakan Aydin

引用次数: 5

bCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets bCharge:大型电动公交车队数据驱动的实时充电调度

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00015

Guang Wang, Xiaoyan Xie, Fan Zhang, Yunhuai Liu, Desheng Zhang

{"title":"bCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets","authors":"Guang Wang, Xiaoyan Xie, Fan Zhang, Yunhuai Liu, Desheng Zhang","doi":"10.1109/RTSS.2018.00015","DOIUrl":"https://doi.org/10.1109/RTSS.2018.00015","url":null,"abstract":"We are witnessing a rapid growth of electrified vehicles because of the ever-increasing concerns over urban air quality and energy security. Compared with other electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to the high owning and operating costs, long charging time, and the uneven distribution of charging facilities. Moreover, the highly dynamic environment factors such as the unpredictable traffic congestions, different passenger demands, and even changing weather, can significantly affect electric bus charging efficiency and potentially hinder further development of large-scale electric bus fleets. To deal with these issues, in this paper, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines and 5,562 bus stops, which is obtained from the Chinese city Shenzhen, who has the first and the largest full electric bus network for public transit. Then we investigate the electric bus network to understand its operating and charging patterns, and further verify the feasibility and necessity of a real-time charging scheduling. With such understanding, we design bCharge, a real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of bCharge, we implement it with the real-world streaming dataset from Shenzhen, which includes GPS data of the electric bus fleet, the bus lines and stops data, coupled with the 376 electric bus charging stations data. The evaluation results show that bCharge can dramatically reduce the charging cost by 23.7% and 12.8% electricity usage simultaneously.","PeriodicalId":294784,"journal":{"name":"2018 IEEE Real-Time Systems Symposium (RTSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128424874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 57

PredJoule: A Timing-Predictable Energy Optimization Framework for Deep Neural Networks PredJoule:一种时间可预测的深度神经网络能量优化框架

2018 IEEE Real-Time Systems Symposium (RTSS) Pub Date : 2018-12-01 DOI: 10.1109/RTSS.2018.00020

Soroush Bateni, Husheng Zhou, Yuankun Zhu, Cong Liu

引用次数: 26